한빛사논문
- 조회352
Kanggeun Lee 1, Dongbin Cho 2, Jinho Jang 3, Kang Choi 2, Hyoung-Oh Jeong 3, Jiwon Seo 2, Won-Ki Jeong 1, Semin Lee 3
1Department of Computer Science and Engineering at Korea University.
2Department of Computer Science at Hanyang University.
3Department of Biomedical Engineering at UNIST.
Kanggeun Lee, Dongbin Cho, and Jinho Jang contributed equally to this work.
Corresponding authors: Jiwon Seo, Won-Ki Jeong, Semin Lee
Abstract
The accurate prediction of cancer drug sensitivity according to the multiomics profiles of individual patients is crucial for precision cancer medicine. However, the development of prediction models has been challenged by the complex crosstalk of input features and the resistance-dominant drug response information contained in public databases. In this study, we propose a novel multidrug response prediction framework, response-aware multitask prediction (RAMP), via a Bayesian neural network and restrict it by soft-supervised contrastive regularization. To utilize network embedding vectors as representation learning features for heterogeneous networks, we harness response-aware negative sampling, which applies cell line-drug response information to the training of network embeddings. RAMP overcomes the prediction accuracy limitation induced by the imbalance of trained response data based on the comprehensive selection and utilization of drug response features. When trained on the Genomics of Drug Sensitivity in Cancer dataset, RAMP achieved an area under the receiver operating characteristic curve > 89%, an area under the precision-recall curve > 59% and an F1 score > 52% and outperformed previously developed methods on both balanced and imbalanced datasets. Furthermore, RAMP predicted many missing drug responses that were not included in the public databases. Our results showed that RAMP will be suitable for the high-throughput prediction of cancer drug sensitivity and will be useful for guiding cancer drug selection processes. The Python implementation for RAMP is available at https://github.com/hvcl/RAMP.
논문정보
- Research article
- 2022년 12월 (BRIC 등록일 2022-12-26)
- 국내 연구진
- 생명과학 > 유전학
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